CoPreMo: A Collaborative Predictive Model in Time Series and Its Application to Radar Target Tracking for ADAS/AD Vehicles

IF 14.3 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-16 DOI:10.1109/TIV.2024.3461730

Zie Eya Ekolle;Ryuji Kohno

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引用次数: 0

Abstract

Automotivedriving assistant systems (ADAS) and Automated driving (AD) technologies are commonly employed in the control of unmanned vehicles along a path. However, like many other technologies, they come with risks, including potential misdirection and collisions with obstacles along the vehicle's route. To mitigate these risks, various tracking systems, including radar tracking systems, are employed to detect and monitor targets along the trajectories of ADAS/AD vehicles. Nevertheless, the effectiveness of these tracking operations is crucial in assessing the reliability of both tracking systems and ADAS/AD technologies, especially at the edge-computing level. In this study, we introduce a tracking technique using a collaborative predictive model in a time series, named CoPreMo, aimed at enhancing the reliability of radar system tracking operations. We conducted three experiments with this model on a simulated radar system to track a target's range at varying speeds across three ADAS/AD scenarios. The experiments yielded range tracking errors of 0.21 m, 0.26 m, and 0.32 m, outperforming the baseline models.

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时间序列协同预测模型CoPreMo及其在ADAS/AD车辆雷达目标跟踪中的应用

汽车驾驶辅助系统（ADAS）和自动驾驶（AD）技术通常用于控制无人驾驶车辆沿路径行驶。然而，像许多其他技术一样，它们也有风险，包括潜在的误导和与车辆沿途障碍物的碰撞。为了降低这些风险，各种跟踪系统，包括雷达跟踪系统，被用来探测和监视沿ADAS/AD车辆轨迹的目标。然而，这些跟踪操作的有效性对于评估跟踪系统和ADAS/AD技术的可靠性至关重要，特别是在边缘计算层面。在本研究中，我们介绍了一种使用时间序列协同预测模型的跟踪技术，名为CoPreMo，旨在提高雷达系统跟踪操作的可靠性。我们在模拟雷达系统上对该模型进行了三次实验，以跟踪三种ADAS/AD场景下不同速度下的目标距离。实验得到的距离跟踪误差分别为0.21 m、0.26 m和0.32 m，优于基线模型。

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来源期刊

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.